Random-normal is similar to the primitive random, in that it randomly generates and reports a number. However, random and random-normal differ in the distribution that they generate. Random will produce a uniform distribution, which means every number has an equal chance of being generated, and there will be roughly the same amount of every number. So in random 4, 0, 1, 2, and 3 are all equally likely to be generated, and after many repetitions of random 4, there will be an equal number of 0’s, 1’s, 2’s, and 3’s.

But random-normal will produce a normal distribution, which looks like a bell curve with many values in the middle and fewer values on the lower and upper end. Normal distributions are found more often in nature than uniform distributions, and can add accuracy to your model. It takes the form random-normal mean standard-deviation. So random-normal 5 1 would represent a normal distribution with a mean of 5 and a standard deviation of 1; numbers close to 5 are much more likely to be generated than 3 or 7, which are two standard deviations away from the mean.